





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)


UpperCamelCase__           =			{
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}

class 							__SCREAMING_SNAKE_CASE       (  _UpperCAmelCase							):
							snake_case  :   Any      =							"""codegen"""
							snake_case  :   Dict      =							{
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(    self	,			__lowerCAmelCase=50400	,			__lowerCAmelCase=2048	,			__lowerCAmelCase=2048	,			__lowerCAmelCase=4096	,			__lowerCAmelCase=28	,			__lowerCAmelCase=16	,			__lowerCAmelCase=64	,			__lowerCAmelCase=None	,			__lowerCAmelCase="gelu_new"	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=1E-5	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=True	,			__lowerCAmelCase=50256	,			__lowerCAmelCase=50256	,			__lowerCAmelCase=False	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      n_ctx
														UpperCamelCase__					  =      n_positions
														UpperCamelCase__					  =      n_embd
														UpperCamelCase__					  =      n_layer
														UpperCamelCase__					  =      n_head
														UpperCamelCase__					  =      n_inner
														UpperCamelCase__					  =      rotary_dim
														UpperCamelCase__					  =      activation_function
														UpperCamelCase__					  =      resid_pdrop
														UpperCamelCase__					  =      embd_pdrop
														UpperCamelCase__					  =      attn_pdrop
														UpperCamelCase__					  =      layer_norm_epsilon
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      use_cache

														UpperCamelCase__					  =      bos_token_id
														UpperCamelCase__					  =      eos_token_id

														super().__init__(
														    bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			tie_word_embeddings=__lowerCAmelCase	,			**__lowerCAmelCase	)






class 							__SCREAMING_SNAKE_CASE       (  _UpperCAmelCase							):






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = "default"	,			__lowerCAmelCase = None	,			__lowerCAmelCase = False	,			):
														super().__init__(__lowerCAmelCase	,			task=__lowerCAmelCase	,			patching_specs=__lowerCAmelCase	,			use_past=__lowerCAmelCase	)
														if not getattr(self._config	,			"""pad_token_id"""	,			__lowerCAmelCase	):
																					# TODO: how to do that better?
																					UpperCamelCase__					  =      0






							@property
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}	)
														if self.use_past:
																					self.fill_with_past_key_values_(__lowerCAmelCase	,			direction="""inputs"""	)
																					UpperCamelCase__					  =      {0: """batch""", 1: """past_sequence + sequence"""}
														else:
																					UpperCamelCase__					  =      {0: """batch""", 1: """sequence"""}

														return common_inputs






							@property
							def 					_lowerCamelCase		(    self	):
														return self._config.n_layer






							@property
							def 					_lowerCamelCase		(    self	):
														return self._config.n_head






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = -1	,			__lowerCAmelCase = -1	,			__lowerCAmelCase = False	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      super(__lowerCAmelCase	,			self	).generate_dummy_inputs(
														    __lowerCAmelCase	,			batch_size=__lowerCAmelCase	,			seq_length=__lowerCAmelCase	,			is_pair=__lowerCAmelCase	,			framework=__lowerCAmelCase	)

														# We need to order the input in the way they appears in the forward()
														UpperCamelCase__					  =      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}	)

														# Need to add the past_keys
														if self.use_past:
																					if not is_torch_available():
																												raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
																					else:
																												import torch

																												UpperCamelCase__  ,  UpperCamelCase__					  =      common_inputs["""input_ids"""].shape
																												# Not using the same length for past_key_values
																												UpperCamelCase__					  =      seqlen + 2
																												UpperCamelCase__					  =      (
																												    batch,
																												    self.num_attention_heads,
																												    past_key_values_length,
																												    self._config.hidden_size // self.num_attention_heads,
																												)
																												UpperCamelCase__					  =      [
																												    (torch.zeros(__lowerCAmelCase	), torch.zeros(__lowerCAmelCase	)) for _ in range(self.num_layers	)
																												]

														UpperCamelCase__					  =      common_inputs["""attention_mask"""]
														if self.use_past:
																					UpperCamelCase__					  =      ordered_inputs["""attention_mask"""].dtype
																					UpperCamelCase__					  =      torch.cat(
																					    [ordered_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase	,			__lowerCAmelCase	,			dtype=__lowerCAmelCase	)]	,			dim=1	)

														return ordered_inputs







							@property
							def 					_lowerCamelCase		(    self	):
														return 13











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   =  {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RobertaPreLayerNormForCausalLM",
           "RobertaPreLayerNormForMaskedLM",
           "RobertaPreLayerNormForMultipleChoice",
           "RobertaPreLayerNormForQuestionAnswering",
           "RobertaPreLayerNormForSequenceClassification",
           "RobertaPreLayerNormForTokenClassification",
           "RobertaPreLayerNormModel",
           "RobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRobertaPreLayerNormForCausalLM",
           "TFRobertaPreLayerNormForMaskedLM",
           "TFRobertaPreLayerNormForMultipleChoice",
           "TFRobertaPreLayerNormForQuestionAnswering",
           "TFRobertaPreLayerNormForSequenceClassification",
           "TFRobertaPreLayerNormForTokenClassification",
           "TFRobertaPreLayerNormMainLayer",
           "TFRobertaPreLayerNormModel",
           "TFRobertaPreLayerNormPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "FlaxRobertaPreLayerNormForCausalLM",
           "FlaxRobertaPreLayerNormForMaskedLM",
           "FlaxRobertaPreLayerNormForMultipleChoice",
           "FlaxRobertaPreLayerNormForQuestionAnswering",
           "FlaxRobertaPreLayerNormForSequenceClassification",
           "FlaxRobertaPreLayerNormForTokenClassification",
           "FlaxRobertaPreLayerNormModel",
           "FlaxRobertaPreLayerNormPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






